SAR remote-sensing image oil spilling detection and identification method

A recognition method and remote sensing image technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve problems such as inability to accurately detect and identify oil spill areas in images, multiplicative noise interference, etc., to achieve good gain effects, The effect of high detection and recognition accuracy

Active Publication Date: 2015-09-30
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology

[0004] In order to solve the problem that the interference caused by multiplicative noise cannot accurately detect and identify the oil spill area in the image, the present invention proposes a relatively effective SAR image oil spill detection and identification method

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  • SAR remote-sensing image oil spilling detection and identification method
  • SAR remote-sensing image oil spilling detection and identification method
  • SAR remote-sensing image oil spilling detection and identification method

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Embodiment Construction

[0019] The implementation of the method of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0020] The design concept of the present invention is: remove the influence of multiplicative noise in the SAR image through Gamma MAP filtering, improve the watershed algorithm to perform large field of view segmentation for homogenous region extraction, C-V (level set) algorithm for the extraction of dark regions in the ocean, through The visual frequency histogram removes false alarms from the extracted dark areas, and finally uses the MRF (Markov Random Field) model of contextual information to further remove false alarms from contextual information to complete oil spill detection and recognition in large fields of view of SAR remote sensing images .

[0021] Such as figure 1 As shown, a kind of SAR remote sensing image oil spill detection and identification method of the present invention, its concrete steps comprise:

[002...

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Abstract

The invention provides an SAR remote-sensing image oil spilling detection and identification method, which has the concrete process comprising the following steps of utilizing a Gamma MAP filter to filter an SAR image, and filtering Sobel; carrying out a watershed algorithm on a gradient map obtained through filtering Sobel to realize sea and land division; utilizing a mean value of a sea surface area image to fill a land area, and then utilizing a C-V algorithm to carry out target area division and extraction in the same homogeneous area on the filled image; extracting a gray-level co-occurrence matrix, the texture property of wavelet decomposition, the gray-level feature and the shape feature of a target area to build a vision frequency histogram; utilizing an SVM classifier model obtained through training to classify the vision frequency histogram, removing a suspected oil spilling area from the target area, and realizing initial false-alarm removal; adopting a result of false-alarm removal as an initial labeling field; utilizing a characteristic field in a context model of MRF to carry out further false-alarm removal based on the initial labeling field, so that the SAR remote-sensing image oil spilling detection and identification method is realized.

Description

technical field [0001] The invention belongs to the technical field of image detection and recognition, and in particular relates to a SAR remote sensing image oil spill detection and recognition method. Background technique [0002] Image detection and recognition is a very widely used technology. In all fields involving image processing, it is ultimately necessary to perform image detection and recognition. It is an image processing system. It contains many aspects of image processing technology, just to finally achieve the purpose of detection and recognition. Therefore, image detection and recognition systems are often generally similar in process, and image preprocessing, image segmentation, target feature extraction, (target training and detection) classifier training, and target recognition and false alarm removal are required respectively. But for each system branch step, different methods can be used to achieve the expected image processing purpose. [0003] For t...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/46G06K9/40
CPCG06V10/30G06V10/50G06V2201/07G06F18/241
Inventor 陈禾庄胤毕福昆陈亮龙腾
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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